Method and apparatus for measuring body age

Abstract

The application belongs to the technical field of data recognition, and provides a body age measuring method and device, the body age measuring method comprising: obtaining basic body data and current body composition data of a user to be detected; calculating a matching degree of the user to be detected in a matching library according to the basic body data and the current body composition data; and calculating a body age of the user to be detected according to the matching degree. The body age measuring method provided by the application can effectively overcome the problem in the prior art that only a single or small number of indexes are used to evaluate the body condition and the real age of the body cannot be fully reflected.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of data recognition, and particularly relates to a body age measurement method and device. BACKGROUND

[0002] With the improvement of people's living standards and the enhancement of health awareness, more and more people begin to pay attention to their own physical condition, especially through various health monitoring means to evaluate and improve the health condition of the body. In this context, the body fat scale as a widely used intelligent device for family health management has become an important tool for many people to monitor their physical health data daily due to its convenience, fast and non-invasive characteristics.

[0003] The traditional body fat scale mainly relies on measuring the basic body composition of the human body, such as body weight, body fat rate, muscle mass, etc. However, although these data can provide a certain degree of health information, they cannot accurately reflect the actual health age of the human body, especially the difference between the body and the actual age. In fact, the "health age" of the human body is not only determined by the actual age, but also closely related to various components of the body, metabolic capacity and other factors. In the existing technology, most health management methods only evaluate according to basic parameters such as age, gender, or simply rely on a certain type of body data, which cannot comprehensively measure the true state of the body. SUMMARY

[0004] Therefore, the embodiments of the present application provide a body age measurement method and device to solve the technical problem that the traditional technology cannot comprehensively measure the true state of the body.

[0005] The first aspect of the embodiments of the present application provides a body age measurement method, which comprises:

[0006] obtaining basic body data and current body composition data of a user to be detected; the basic body data includes actual age, gender, height and weight, and the current body composition data includes body fat rate, muscle mass, water content, total fat amount, bone content, visceral fat content and basal metabolic rate;

[0007] calculating the matching degree of the user to be detected in the matching library according to the basic body data and the current body composition data;

[0008] calculating the body age of the user to be detected according to the matching degree.

[0009] Further, the step of calculating the matching degree of the user to be detected in the matching library according to the basic body data and the current body composition data comprises:

[0010] constructing actual age, gender, height and weight in the basic body data into a first body feature vector;

[0011] constructing body fat rate, muscle mass, basal metabolic rate, water content, total fat mass, bone content, visceral fat content and basal metabolic rate in the current body composition data into a second body feature vector;

[0012] obtaining the first preset feature vector and the second preset feature vector corresponding to each of the plurality of preset sample data in the matching library;

[0013] calculating a first similarity between the first body feature vector and the first preset feature vector;

[0014] calculating a second similarity between the second body feature vector and the second preset feature vector;

[0015] According to the first similarity and the second similarity corresponding to each of the plurality of preset sample data, the matching degree corresponding to each of the plurality of preset sample data is calculated.

[0016] Further, the step of calculating the matching degree corresponding to each of the plurality of preset sample data according to the first similarity and the second similarity corresponding to each of the plurality of preset sample data comprises:

[0017] If the first similarity is greater than a first threshold, the first similarity is multiplied by a first preset weight to obtain a first value;

[0018] The second similarity is multiplied by a second preset weight to obtain a second value;

[0019] The first value and the second value are added to obtain the matching degree;

[0020] If the first similarity is not greater than the first threshold, the matching degree is set to 0.

[0021] Further, the step of calculating the body age of the to-be-detected user according to the matching degree comprises:

[0022] When the matching degree is greater than a second threshold, the reference body age corresponding to the maximum matching degree is taken as the body age of the to-be-detected user;

[0023] When the matching degree is not greater than the second threshold, the body age of the to-be-detected user is calculated through a preset model.

[0024] Further, the step of taking the reference body age corresponding to the maximum matching degree as the body age of the to-be-detected user when the matching degree is greater than the second threshold comprises:

[0025] When the matching degree is greater than a second threshold, an initial body age corresponding to the maximum matching degree and a plurality of reference body composition data are obtained;

[0026] The reference body composition data corresponding to the same kind of body composition data and the current body composition data are subtracted to obtain a data difference value;

[0027] If the data difference value is greater than a third threshold, the reference body composition data corresponding to the data difference value is taken as the to-be-adjusted reference body composition data;

[0028] A body age influence factor corresponding to the to-be-adjusted reference body composition data is obtained;

[0029] The initial body age is adjusted to obtain the reference body age according to the to-be-adjusted reference body composition data, the current body composition data and the body age influence factor;

[0030] The reference body age is taken as the body age of the to-be-detected user.

[0031] Further, the step of adjusting the initial body age to obtain the reference body age according to the to-be-adjusted reference body composition data, the current body composition data and the body age influence factor comprises:

[0032] The to-be-adjusted reference body composition data is multiplied by the body age influence factor to obtain a first age influence value;

[0033] The current body composition data is multiplied by the body age influence factor to obtain a second age influence value;

[0034] The first age influence value is subtracted from the second age influence value to obtain an age adjustment value;

[0035] The initial body age is subtracted from the age adjustment value to obtain the reference body age.

[0036] Further, the step of calculating the body age of the to-be-detected user through a preset model when the matching degree is not greater than a first threshold comprises:

[0037] A body fat rate, a muscle mass, a water content, a total fat amount, a bone content, an internal fat content and a basal metabolic rate are input into a preset model to obtain a body age output by the preset model;

[0038] The preset model is: ; ; wherein, represents the body age, represents the actual age, represents the body fat rate, represents muscle mass, represents basal metabolic rate, represents water content, represents total fat mass, represents bone content, represents visceral fat content, , , , , , and represents a tuning factor for each body composition data item, represents a tuning factor for an interaction term, represents a non-linear function of the i-th current body composition data , represents the i-th current body composition data, represents a standard value corresponding to the current body composition data, and represents a weight factor.

[0039] A second aspect of the embodiment of the present application provides a body age measuring device, comprising:

[0040] an acquisition unit configured to acquire basic body data and current body composition data of a user to be detected, wherein the basic body data comprises actual age, gender, height and weight, and the current body composition data comprises body fat rate, muscle mass, water content, total fat mass, bone content, visceral fat content and basal metabolic rate;

[0041] a first calculation unit configured to calculate a matching degree of the user to be detected in a matching library according to the basic body data and the current body composition data;

[0042] a second calculation unit configured to calculate a body age of the user to be detected according to the matching degree.

[0043] A third aspect of the embodiment of the present application provides a terminal device, comprising a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps in the body age measuring method of the first aspect.

[0044] A fourth aspect of the embodiment of the present application provides a computer readable storage medium, wherein the computer readable storage medium stores a computer program, and the computer program is executable on a processor to implement the steps in the body age measuring method of the first aspect.

[0045] Compared with the prior art, the embodiment of the present application has the beneficial effect that: by comprehensively obtaining the basic body data (including actual age, gender, height and weight) and the current body composition data (including body fat rate, muscle mass, water content, total fat amount, bone content, visceral fat content and basal metabolic rate) of the user, and combining the data in the matching library for matching degree calculation, the present application can evaluate the body state of the user in multiple dimensions. The method matches the similarity between the current body state of the user and a large number of standard samples, and then obtains a more representative "body age". Compared with the traditional evaluation method based on weight or body fat rate, the measurement method provided by the present application is more comprehensive and scientific, and can significantly improve the accuracy of body age evaluation and the recognition ability of individual differences. The measurement method of body age provided by the present application can effectively overcome the problem that the prior art relies on only a single or a small number of indicators to evaluate the body condition, and cannot fully reflect the real age of the body. BRIEF DESCRIPTION OF DRAWINGS

[0046] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the embodiments or related technical descriptions will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0047] Figure 1 A schematic flow chart of a measurement method of body age provided by the present application is shown;

[0048] Figure 2 A schematic diagram of a measurement device of body age provided by an embodiment of the present application is shown;

[0049] Figure 3 A schematic diagram of a terminal device provided by an embodiment of the present application is shown. DETAILED DESCRIPTION

[0050] In the following description, specific details are set forth in order to provide a thorough understanding of embodiments of the present application. However, persons skilled in the art will understand that the present application can be practiced without these specific details. In other instances, well-known systems, devices, circuits and methods have not been described in detail so as not to obscure the present application.

[0051] The embodiment of the present application provides a measurement method and device of body age to solve the technical problem that the conventional technology cannot comprehensively measure the real state of the body.

[0052] Firstly, the present application provides a measurement method of body age. Please refer toFigure 1 , Figure 1 A schematic flowchart of a method for measuring body age is shown. As shown, the method for measuring body age can include the following steps: Figure 1

[0053] Step 101: Obtain the basic body data and current body composition data of the user to be detected; the basic body data includes actual age, gender, height and weight, and the current body composition data includes body fat rate, muscle mass, water content, total fat mass, bone content, visceral fat content and basal metabolic rate;

[0054] First, the basic information and body composition information of the user need to be collected, which is the basis for evaluating physical health. The current body composition data includes:

[0055] Body fat rate refers to the proportion of body fat in body weight. Higher body fat rate usually means higher risk of disease. Muscle mass refers to the total weight of muscle in the body. The higher the muscle mass, the higher the metabolic rate, which helps maintain health. Water content refers to the proportion of water in the body. Water level affects the body's electrolyte balance, blood circulation and other health conditions. Total fat mass refers to the total weight of fat in the body. It is an important indicator for measuring body fat content. Bone content refers to the weight of the skeleton in the body. Bone density is crucial to physical health, especially in preventing problems such as osteoporosis. Visceral fat content refers to the fat content around the internal organs in the body. More visceral fat increases the risk of diabetes, cardiovascular disease and other diseases. Basal metabolic rate (BMR) refers to the energy consumed by the body to maintain basic physiological functions in a resting state. Higher BMR usually means higher metabolic level and more energy consumption.

[0056] Step 102: Calculate the matching degree of the user to be detected in the matching library according to the basic body data and the current body composition data;

[0057] This process is to compare the collected basic data and body composition data with the "matching library", which contains a large number of health standards and body age and body composition data of different age, gender and other groups.

[0058] By comparing the user's body data with the data in the matching library, the similarity between the user's physical health status and the normal standard or target state can be evaluated.

[0059] Specifically, step 102 specifically includes steps 1021 to 1026:

[0060] Step 1021: Construct the actual age, gender, height and weight in the basic body data into a first body feature vector;

[0061] ​In this step, the user's basic body data (actual age, gender, height, and weight) needs to be first converted into a "feature vector". The feature vector is a numerical vector used to represent the user's physical characteristics for data analysis.

[0062] Actual age, gender, height, and weight are important data closely related to an individual's health status. By converting these data into a vector, subsequent calculations and comparisons can be facilitated.

[0063] Actual age can be directly added to the vector as a numerical value. Gender can be encoded using binary coding, such as 1 for male and 0 for female, or can be encoded using one-hot encoding. Height and weight can be directly added to the vector as numerical values.

[0064] For example, if a user's actual age is 30 years old, gender is female, height is 165 cm, and weight is 60 kg, the first body feature vector can be represented as: [30, 0, 165, 60] (where 0 represents female, 30 is the actual age, and 165 and 60 are the numerical values of height and weight).

[0065] Step 1022: Constructing body fat percentage, muscle mass, basal metabolic rate, water content, total fat, bone content, visceral fat content, and basal metabolic rate in the current body composition data into a second body feature vector;

[0066] Next, the user's body composition data (body fat percentage, muscle mass, basal metabolic rate, water content, total fat, bone content, visceral fat content) needs to be converted into another feature vector. These data are related to the specific manifestations of physical health, and after being converted into a feature vector, they can help compare and evaluate the user's physical health level.

[0067] Body fat percentage, muscle mass, basal metabolic rate, and other data are usually presented in percentage or weight numerical form and can be directly converted into numerical values. Water content, total fat, bone content, and visceral fat content can also be represented in numerical form and added to the second feature vector.

[0068] For example, if a user's body fat percentage is 25%, muscle mass is 35 kg, basal metabolic rate is 1500 kcal, water content is 50%, total fat is 20 kg, bone content is 5 kg, and visceral fat content is 10, the second body feature vector may be represented as: [25, 35, 1500, 50, 20, 5, 10].

[0069] Step 1023: Obtain the first preset feature vector and the second preset feature vector corresponding to each of the plurality of preset sample data in the matching library;

[0070] In this step, a plurality of preset sample data is obtained from a previously prepared "matching library". Each sample data already contains a corresponding "first preset feature vector" and "second preset feature vector".

[0071] The matching library is a collection of a large number of pre-collected individual data, which can come from healthy people or typical data extracted from medical research. Each sample contains a first body feature vector and a second body feature vector similar to the above.

[0072] Step 1024: Calculate the first similarity between the first body feature vector and the first preset feature vector;

[0073] The goal of this step is to calculate the similarity between the "first body feature vector" of the user to be detected and the "first preset feature vector" of each preset sample in the matching library. The similarity calculation methods include Euclidean distance, cosine similarity, etc.

[0074] Step 1025: Calculate the second similarity between the second body feature vector and the second preset feature vector;

[0075] Similar to the first similarity, the similarity calculation between the second body feature vector and the second preset feature vector is based on body composition data such as body fat rate, muscle mass, basal metabolic rate, etc.

[0076] The second similarity can evaluate the similarity between the two vectors by quantifying the differences in body composition between the user to be detected and the samples in the matching library. For example, if the user and the sample are similar in body fat rate, muscle mass, etc., their second similarity is higher.

[0077] Step 1026: According to the first similarity and the second similarity corresponding to each preset sample data, calculate the matching degree corresponding to each preset sample data.

[0078] Finally, according to the calculated first similarity and second similarity, combined with the weighted average or other methods of these similarities, the matching degree of each matching sample is calculated.

[0079] The matching degree is a comprehensive index that measures the similarity between the body features of the user to be detected and the preset samples in the matching library. By combining the first similarity and the second similarity, a final matching degree value can be obtained, reflecting the overall similarity between the user and the matching sample.

[0080] In the embodiments corresponding to steps 1021 to 1026, through this calculation process, the matching degree between the to-be-detected user and the plurality of preset samples can be finally obtained, thereby providing data support for subsequent body age calculation.

[0081] Specifically, step 1026 specifically includes steps A1 to A4:

[0082] Step A1: If the first similarity is greater than a first threshold, multiplying the first similarity by a first preset weight to obtain a first value;

[0083] Here, the first similarity is the similarity between the to-be-detected user and the preset sample in the matching library in the basic body data. In order to give different weights to different similarities when calculating the matching degree, a first preset weight is needed to weight it. The first preset weight is a value used to adjust the importance or contribution of the basic body data similarity in the calculation of the matching degree. The setting of the weight reflects the importance of the basic body data in the evaluation of body health or age.

[0084] The first threshold is a preset critical value. If the first similarity is greater than this threshold, it means that the to-be-detected user has a higher similarity with the matching sample in the basic body data, which is worthy of further calculation of the matching degree. If the first similarity is less than or equal to the first threshold, the subsequent calculation is not performed, and the matching degree is directly set to 0.

[0085] Step A2: multiplying the second similarity by a second preset weight to obtain a second value;

[0086] Similarly, the second similarity is the similarity between the to-be-detected user and the preset sample in the matching library in the body composition data (such as body fat rate, muscle mass, basal metabolic rate, etc.). If the second similarity is high, it means that the user has a high similarity with the sample in the body composition, which usually also plays an important role in the evaluation of body age.

[0087] The second preset weight is used to adjust the weight of the body composition data similarity in the calculation of the matching degree. Generally, body composition data (such as body fat rate, muscle mass, etc.) may be more directly related to the evaluation of body health than basic data, and therefore may also be given a certain weight.

[0088] Step A3: adding the first value and the second value to obtain the matching degree;

[0089] The matching degree is the comprehensive similarity between the to-be-detected user and a certain preset sample. In this scheme, the matching degree is calculated by combining the weighted similarities of the basic body data and the body composition data. The addition of the weighted similarities (the first value and the second value) means that the relative importance of the two is included in the final result.

[0090] Step A4: If the first similarity is not greater than a first threshold, set the matching degree to 0.

[0091] This part is used to deal with the case where the first similarity does not meet the requirements. When the first similarity is less than or equal to the first threshold, it means that the performance of the to-be-detected user on the basic body data is quite different from the preset sample, and it is not worth further calculating the matching degree at this time. Therefore, the matching degree is set to 0.

[0092] The meaning of this rule is that by setting a threshold, it can avoid invalid matching for samples with too large differences in basic data, and ensure that the calculated matching degree is meaningful. If the basic data similarity is too low, the result of continuing to calculate the body composition similarity may also be inaccurate, so the matching degree is directly set to 0.

[0093] In the embodiments corresponding to steps A1 to A4, this process can help the system more accurately evaluate the health similarity between the to-be-detected user and the sample, and ultimately provide a more reliable basis for subsequent body age calculation through the matching degree.

[0094] Step 103: According to the matching degree, calculate the body age of the to-be-detected user.

[0095] Body age refers to how the individual's physiological state compares to the health status of the same age group. If the user's body composition (such as body fat rate, muscle mass, etc.) performs better compared to the standard of their actual age, their body age may be lower than their actual age; conversely, if the body health status is poor, the body age may be higher than the actual age.

[0096] For example, if a 30-year-old person has a body fat rate of 40 years old and muscle mass below the normal range, the calculated body age may be greater than 30 years old, reflecting the actual situation of their physical health status.

[0097] Specifically, step 103 specifically includes steps 1031 to 1032:

[0098] Step 1031: When the matching degree is greater than a second threshold, the reference body age corresponding to the maximum matching degree is taken as the body age of the to-be-detected user.

[0099] If the matching degree is high enough, i.e. greater than a preset second threshold, it is considered that the body state of the to-be-detected user is highly similar to the body state of a certain sample.

[0100] Specifically, step 1031 specifically includes steps B1 to B6:

[0101] Step B1: When the matching degree is greater than the second threshold, obtaining the initial body age corresponding to the maximum matching degree and a plurality of reference body composition data;

[0102] When the matching degree of the to-be-detected user is greater than the second threshold, it means that the similarity between the to-be-detected user and the matching sample is high. Therefore, the system selects the sample with the highest similarity to the to-be-detected user, and obtains the initial body age of the sample (that is, the reference body age of the sample) and a plurality of reference body composition data (such as body fat rate, muscle mass, bone density, etc.).

[0103] Maximum matching degree: corresponds to the sample most similar to the to-be-detected user, whose reference body age and body composition data best represent the body state of the to-be-detected user.

[0104] Initial body age: before any adjustment, this value directly comes from the sample most similar to the to-be-detected user.

[0105] Reference body composition data: including the body composition of the sample (such as body fat rate, muscle mass, bone density, etc.), these data help to accurately adjust the body age.

[0106] Step B2: Subtract the reference body composition data corresponding to the same body composition data from the current body composition data to obtain a data difference;

[0107] In this part, the system compares the body composition data of the selected sample with the current body composition data of the to-be-detected user. Specifically, the system will calculate the difference value of the same type of body composition (such as body fat rate, muscle mass, etc.).

[0108] Step B3: If the data difference is greater than the third threshold, the reference body composition data corresponding to the data difference is taken as the to-be-adjusted reference body composition data;

[0109] If the difference is greater than the third threshold, the system will mark this body composition data as to-be-adjusted reference body composition data.

[0110] Step B4: Obtain the body age influence factor corresponding to the to-be-adjusted reference body composition data;

[0111] In this step, the system will obtain a "body age influence factor" according to the body composition data with large difference. This influence factor is a numerical value, indicating the influence degree of a specific body composition (such as body fat rate or muscle mass) on body age.

[0112] Body age influence factor is calculated by statistical analysis or model, which represents how the change of a certain body composition affects the body age. For example, the body fat rate may increase 1 year of body age for every 1% increase.

[0113] Step B5: adjusting the initial body age to obtain the reference body age according to the reference body composition data to be adjusted, the current body composition data and the body age influence factor;

[0114] The adjustment process will appropriately correct the initial body age according to the difference in body composition to obtain a more accurate reference body age.

[0115] Specifically, step B5 specifically includes steps B51 to B54:

[0116] Step B51: multiplying the reference body composition data to be adjusted by the body age influence factor to obtain a first age influence value;

[0117] By multiplying the reference body composition data to be adjusted by the influence factor, a "first age influence value" can be obtained, which represents the adjustment amount of the body composition to be adjusted to the body age.

[0118] Step B52: multiplying the current body composition data by the body age influence factor to obtain a second age influence value;

[0119] By multiplying the current body composition data by the body age influence factor, a "second age influence value" can be obtained, which represents the influence of the current body composition of the user to be detected on the body age.

[0120] Step B53: subtracting the first age influence value from the second age influence value to obtain an age adjustment value;

[0121] In this step, the system subtracts the first age influence value from the second age influence value to obtain an "age adjustment value". The first age influence value and the second age influence value reflect the influence of the reference body composition data and the current body composition data on the body age. The purpose of subtraction is to measure the difference between the current body composition of the user to be detected and the reference body composition, and then adjust the initial body age.

[0122] Step B54: subtracting the initial body age from the age adjustment value to obtain the reference body age.

[0123] Finally, the system will adjust the initial body age according to the "age adjustment value" to obtain the final reference body age. The initial body age is a preliminary body age assessment based on the maximum matching degree sample, which has not yet considered the difference adjustment. The age adjustment value is the body age adjustment amount obtained by comparing the reference body composition and the current body composition. By subtracting the age adjustment value from the initial body age, the reference body age can be obtained, which will more accurately reflect the actual physical condition of the user to be detected.

[0124] In the embodiments corresponding to steps B51 to B54, this process mainly adjusts the reference body age by comparing the body composition data of the user to be detected and the reference samples, calculating the adjustment value of the body composition difference using the body age influencing factor, and finally adjusting the initial body age to obtain a more accurate reference body age. This method provides a fine adjustment mechanism that can consider the influence of different body compositions while ensuring that the final body age is more in line with the actual physical state of the user.

[0125] Step B6: Taking the reference body age as the body age of the user to be detected.

[0126] The system takes the adjusted reference body age as the final body age result of the user to be detected. This body age better reflects the true physical condition of the user to be detected, as it has been adjusted according to the differences with the sample with the maximum matching degree.

[0127] In the embodiments corresponding to steps B1 to B6, the goal of this implementation scheme is to obtain a more accurate body age assessment by comparing the differences in body composition data between the user to be detected and the reference samples, and adjusting the initial body age according to these differences. Through these steps, the system can balance between accuracy and flexibility, ensuring that the final body age is more in line with the actual health status of the user to be detected.

[0128] Step 1032: When the matching degree is not greater than the second threshold, calculating the body age of the user to be detected by a preset model.

[0129] If the matching degree of the user to be detected is lower than or equal to the second threshold, it means that the similarity between the physical characteristics of the user to be detected and the samples in the matching library is low, and the body age cannot be directly obtained by referring to the body age. Therefore, the system will use a preset model to calculate the body age of the user to be detected.

[0130] The preset model is a pre-trained model that can predict the body age based on the user's body data (such as basic body data, body composition data, etc.).

[0131] Specifically, step 1032 specifically includes:

[0132] inputting the body fat rate, muscle mass, water content, total fat amount, bone content, visceral fat content, and basal metabolic rate into the preset model to obtain the body age output by the preset model;

[0133] The preset model is: ; ; wherein, represents the body age, represents the actual age, represents the body fat rate, represents muscle mass, represents basal metabolic rate, represents water content, represents total fat mass, represents bone content, represents visceral fat content, , , , , , and represents the tuning factor for each body composition data item, represents the tuning factor for interaction terms, represents the non-linear function for the i-th current body composition data , represents the i-th current body composition data, represents the standard value corresponding to the current body composition data, and represents the weight factor. The design of the pre-set model is based on a multi-dimensional, non-linear model, aiming to comprehensively consider various body composition data (such as body fat rate, muscle mass, water content, etc.) and their complex interrelationships, so as to accurately calculate the body age.

[0134] Each current body composition data is fed into a non-linear function to amplify its asymmetric influence on body age. Normalization of different data scales. Each component item is processed through a non-linear function, aiming to convert the relationship between each component and body age into a mathematical form more suitable for physiological changes. The logarithmic function is used to process variables with different dimensions and scales (such as body fat rate and muscle mass), avoiding the dominance of certain variables due to the large number of orders of magnitude. The influence of "deviation from the healthy standard" is expressed through a square term, which will increase asymmetrically. For example, when the body fat rate exceeds the healthy level, it will have a greater impact on body age. The is used to quantify the relative deviation of each body composition from its healthy reference value. The greater the deviation, the more significant the impact of this item on body age. The purpose of the cross-term is to capture the interaction between different body compositions, i.e.:

[0135] Body fat rate combined with visceral fat (VF): Body fat rate and visceral fat are closely related, especially in individuals with high visceral fat, the ratio of fat to muscle exacerbates the negative impact on physical health.

[0136] Total fat to water ratio: Excessive fat can affect the body's water balance. Fat increase can cause edema or dehydration, thus accelerating the aging of the body.

[0137] This part of the interaction is to enhance the expression ability of the model, so that it can accurately simulate the complex physiological relationship between the components in the human body.

[0138] In the formula, the tuning factor 、 、 、 、 、 、 、 and are key parts of adaptive adjustment according to the specific physiological characteristics, gender, age, activity level, etc. of the individual. The role of these tuning factors includes:

[0139] Personalized adjustment: According to the gender, age, health status, etc. of the individual, automatically adjust the weight of each component on the body age. For example, men and women have different fat distribution, muscle mass and metabolic characteristics, so different tuning factors are needed.

[0140] Optimize the impact of body composition: Through the tuning factor, the preset model can accurately assess the impact of each body composition on the individual, avoiding excessive simplification of the judgment of physical health.

[0141] Since each body component and the relationship between components are modeled through complex nonlinear functions and interaction terms, this preset model can accurately reflect the actual health status and physiological age of the body. By introducing the tuning factor, the preset model can be personalized for different individuals, adapting to different ages, genders, lifestyles, etc. Not only the impact of a single component, but also the interaction between multiple body components is considered by the preset model, and further optimized through metabolic, bone density and other health indicators to calculate the body age.

[0142] In the embodiments corresponding to steps 1031 to 1032, in this way, the system can provide more accurate body age assessment according to different situations, both taking advantage of high matching degree samples and ensuring the rationality of the assessment results through the model in the case of low matching degree.

[0143] It is worth noting that since the sample data in the matching library is based on the body age data measured by precision instruments, it has a higher advantage in data accuracy. The accuracy of the preset model is lower than that of the matching library. Therefore, this embodiment prefers to use the matching library to calculate the body age, and when the sample data with high similarity cannot be matched in the matching library, the preset model is used to calculate the body age, so as to maximize the calculation accuracy.

[0144] In the embodiment corresponding to steps 101 to 103, the body state of the user is evaluated in multiple dimensions by comprehensively obtaining the basic body data (including actual age, gender, height, and weight) and the current body composition data (including body fat rate, muscle mass, water content, total fat amount, bone content, visceral fat content, and basal metabolic rate) of the user, and performing matching degree calculation in combination with the data in the matching library. The method obtains a more representative "body age" by matching the similarity between the current body state of the user and a large number of standard samples. Compared with the traditional evaluation method based on only weight or body fat rate, the measurement method provided by the application is more comprehensive and scientific, and can significantly improve the accuracy of body age evaluation and the recognition ability of individual differences. The measurement method of body age provided by the application can effectively overcome the problem that the prior art relies only on a single or a small number of indicators to evaluate the body condition and cannot fully reflect the real age of the body.

[0145] As Figure 2 The application provides a body age measurement device, please see Figure 2 , Figure 2 The application provides a body age measurement device, please see Figure 2 The body age measurement device comprises:

[0146] The acquisition unit 21 is configured to acquire the basic body data and the current body composition data of the user to be detected, wherein the basic body data comprises actual age, gender, height, and weight, and the current body composition data comprises body fat rate, muscle mass, water content, total fat amount, bone content, visceral fat content, and basal metabolic rate.

[0147] The first calculation unit 22 is configured to calculate the matching degree of the user to be detected in the matching library according to the basic body data and the current body composition data.

[0148] The second calculation unit 23 is configured to calculate the body age of the user to be detected according to the matching degree.

[0149] This invention provides a device for measuring body age. By comprehensively acquiring a user's basic body data (including actual age, gender, height, and weight) and current body composition data (including body fat percentage, muscle mass, water content, total fat, bone mass, visceral fat content, and basal metabolic rate), and combining this data with data from a matching database to calculate the matching degree, this invention can assess a user's physical condition from multiple dimensions. This method derives a more representative "body age" by matching the similarity between the user's current physical condition and a large number of standard samples. Compared to traditional assessment methods that rely solely on weight or body fat percentage, the measurement method provided by this invention is more comprehensive and scientific, significantly improving the accuracy of body age assessment and the ability to identify individual differences. The body age measurement method provided by this invention effectively overcomes the problem of existing technologies that rely on only a single or few indicators to assess physical condition and cannot comprehensively reflect the true age of the body. Figure 3 This is a schematic diagram of a terminal device provided in an embodiment of the present invention. Figure 3 As shown, a terminal device 3 in this embodiment includes: a processor 30, a memory 31, and a computer program 32 stored in the memory 31 and executable on the processor 30, such as a body age measurement program. When the processor 30 executes the computer program 32, it implements the steps in the various body age measurement method embodiments described above, for example... Figure 1 Steps 101 to 103 are shown. Alternatively, when the processor 30 executes the computer program 32, it implements the functions of each unit in the above-described device embodiments, for example... Figure 2 The function of the unit shown.

[0150] For example, the computer program 32 can be divided into one or more units, which are stored in the memory 31 and executed by the processor 30 to complete the present invention. The one or more units can be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of the computer program 32 in the terminal device 3. For example, the specific functions of each unit of the computer program 32 can be divided as follows:

[0151] The acquisition unit is used to acquire the basic body data and current body composition data of the user to be tested; the basic body data includes actual age, gender, height and weight, and the current body composition data includes body fat percentage, muscle mass, water content, total fat, bone content, visceral fat content and basal metabolic rate;

[0152] The first calculation unit is used to calculate the matching degree of the user to be detected in the matching database based on the basic body data and the current body composition data.

[0153] A second computing unit is configured to calculate the physical age of the user to be detected according to the matching degree.

[0154] The terminal device includes but is not limited to a processor 30 and a memory 31. Those skilled in the art can understand that, Figure 3 The terminal device 3 is only an example and does not constitute a limitation on the terminal device 3, and can include more or fewer components than shown, or combine certain components, or different components, for example, the terminal device can also include an input / output device, a network access device, a bus, etc.

[0155] The processor 30 can be a central processing unit (CPU), and can also be other general-purpose processors, digital signal processors (DSP), application specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor.

[0156] The memory 31 can be an internal storage unit of the terminal device 3, such as a hard disk or a memory of the terminal device 3. The memory 31 can also be an external storage device of the terminal device 3, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. Further, the memory 31 can include both the internal storage unit and the external storage device of the terminal device 3. The memory 31 is used to store the computer program and other programs and data required by the terminal device. The memory 31 can also be used to temporarily store data that has been output or will be output.

[0157] It should be understood that the size of the serial number of each step in the above embodiments does not mean the order of execution, and the execution order of each process should be determined according to its function and inherent logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0158] It should be noted that the information interaction, execution process and the like between the above apparatuses / units are based on the same concept as the method embodiments of the present application, and the specific functions and brought technical effects can be referred to the method embodiments part, and will not be repeated here.

[0159] Those skilled in the art can clearly understand that, for the convenience and brevity of description, only the above-mentioned division of each functional unit and module is exemplified, and in actual application, the above-mentioned functions can be completed by different functional units and modules according to needs. The internal structure of the device is divided into different functional units or modules to complete all or part of the functions described above. Each functional unit and module in the embodiment can be integrated in one processing unit, or each unit can exist physically, or two or more units can be integrated in one unit. The integrated unit can be realized in the form of hardware or in the form of software functional unit. In addition, the specific name of each functional unit and module is only for convenient distinction, and does not limit the protection scope of the present application. The specific working process of the unit and module in the system can refer to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0160] The embodiment of the present application further provides a computer readable storage medium, the computer readable storage medium stores a computer program, and the computer program is executed by a processor to realize the steps in each method embodiment.

[0161] The embodiment of the present application provides a computer program product, when the computer program product runs on a mobile terminal, so that the mobile terminal executes to realize the steps in each method embodiment.

[0162] The integrated unit, if implemented in the form of a software function unit and sold or used as an independent product, can be stored in a computer readable storage medium. Based on such understanding, the present application can implement all or part of the processes in the above-mentioned embodiment methods, which can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a computer readable storage medium, and the computer program can implement the steps of each method embodiment when executed by a processor. The computer program includes computer program code, which can be in the form of source code, object code, executable files or some intermediate forms. The computer readable medium can at least include any entity or device capable of carrying the computer program code to the photographing device / terminal equipment, recording medium, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal and software distribution medium, such as U disk, mobile hard disk, magnetic disk or optical disk.

[0163] In the above embodiments, the description of each embodiment has its own focus, and the parts not described or recorded in detail in a certain embodiment can be referred to the related description of other embodiments.

[0164] Those skilled in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be realized by electronic hardware or a combination of computer software and electronic hardware. Whether the functions are realized in hardware or software depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to realize the described functions for each specific application, but such implementation should not be considered beyond the scope of the present application.

[0165] In the embodiments provided by the present application, it should be understood that the disclosed apparatus / network device and method can be implemented in other ways. For example, the above-described apparatus / network device embodiments are merely schematic, for example, the division of the modules or units is only a logical function division, and there can be another division manner in actual implementation, for example, a plurality of units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the shown or discussed mutual elements can be indirect coupling or communication connection through some interfaces, devices or units, and can be electrical, mechanical or other forms.

[0166] The units described as separate components can or can not be physically separate, and the components displayed as separate components can or can not be physical separate, and can be located in one position or distributed on a plurality of network units.

[0167] It should be understood that the term "comprises", "comprising", "includes", "including" and "contains", "containing" when used in the specification and appended claims of this application, indicates the presence of the described features, integers, steps, operations, elements, and / or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof.

[0168] It should also be understood that the term "and / or" when used in the specification and appended claims of this application, means any one and / or all possible combinations of one or more of the associated listed items.

[0169] As used in the description of the application and the appended claims, the term "if" can be interpreted to mean "when" or "upon" or "in response to determining" or "in response to ascertaining", depending on the context. Similarly, the phrase "if it is determined" or "if a determination is made" can be interpreted to mean "upon determining" or "in response to determining" or "upon making a determination" or "in response to making a determination", depending on the context.

[0170] In addition, the terms "first", "second", "third", etc. as used in the description of the application and the appended claims are merely used for distinguishing between similar underlying features. They are not used to designate or imply any relative importance.

[0171] Reference throughout this specification to "one embodiment" or "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the present application. Thus, the appearances of the phrases "in one embodiment" or "in an embodiment" or "in some embodiments" or "in other embodiments" or "in still other embodiments", etc. in various places throughout this specification are not necessarily all referring to the same embodiment, unless otherwise specifically so stated. Furthermore, the term "comprising" or "containing" or "including" as used herein is specifically intended to encompass the presence of stated features, integers, steps, operations, elements, components, or a combination thereof, but does not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, or groups thereof.

[0172] The above-described embodiments are only used to illustrate the technical solutions of the present application, and are not intended to limit the present application; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: it can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacement for part of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application, and should be included in the protection scope of the present application.

Claims

1. A method of measuring the age of a body, characterized by, The body age measurement method comprises: obtaining basic body data and current body composition data of a user to be detected; the basic body data comprises actual age, gender, height and weight, and the current body composition data comprises body fat rate, muscle mass, water content, total fat amount, bone content, visceral fat content and basal metabolic rate; calculating a matching degree of the user to be detected in a matching library according to the basic body data and the current body composition data; when the matching degree is greater than a second threshold, obtaining an initial body age and a plurality of reference body composition data corresponding to the maximum matching degree; subtracting the reference body composition data corresponding to the same body composition data from the current body composition data to obtain a data difference value; if the data difference value is greater than a third threshold, taking the reference body composition data corresponding to the data difference value as adjusted reference body composition data; obtaining a body age influence factor corresponding to the adjusted reference body composition data; wherein the body age influence factor represents the influence degree of the body composition on the body age; multiplying the adjusted reference body composition data by the body age influence factor to obtain a first age influence value; multiplying the current body composition data by the body age influence factor to obtain a second age influence value; subtracting the first age influence value from the second age influence value to obtain an age adjustment value; wherein the first age influence value and the second age influence value reflect the influence of the reference body composition data and the current body composition data on the body age; subtracting the age adjustment value from the initial body age to obtain a reference body age; taking the reference body age as the body age of the user to be detected; inputting the body fat rate, muscle mass, water content, total fat amount, bone content, visceral fat content and basal metabolic rate into a preset model to obtain a body age output by the preset model; the preset model is: ； ； wherein, represents a body age, represents an actual age, represents a body fat rate, represents a muscle mass, represents a basal metabolic rate, represents a water content, represents a total fat amount, represents a bone content, represents a visceral fat content, , , , , , and represents a tuning factor for each body composition data item, represents a tuning factor for an interaction term, represents a non-linear function of the i-th current body composition data , represents the i-th current body composition data, represents a standard value corresponding to the current body composition data, and represents a weight factor.

2. The method of measuring body age according to claim 1, wherein the step of calculating the matching degree of the user to be detected in the matching library according to the basic body data and the current body composition data comprises: constructing the actual age, gender, height and weight in the basic body data into a first body feature vector; constructing the body fat rate, muscle mass, basal metabolic rate, water content, total fat amount, bone content, visceral fat content and basal metabolic rate in the current body composition data into a second body feature vector; obtaining a first preset feature vector and a second preset feature vector corresponding to each of a plurality of preset sample data in the matching library; calculating a first similarity between the first body feature vector and the first preset feature vector; calculating a second similarity between the second body feature vector and the second preset feature vector; calculating the matching degree corresponding to each of the plurality of preset sample data according to the first similarity and the second similarity corresponding to each of the plurality of preset sample data.

3. The method of measuring body age according to claim 2, wherein the step of calculating the matching degree corresponding to each of the plurality of preset sample data according to the first similarity and the second similarity corresponding to each of the plurality of preset sample data comprises: If the first similarity is greater than a first threshold, multiplying the first similarity by a first preset weight to obtain a first numerical value; Multiplying the second similarity by a second preset weight to obtain a second numerical value; Adding the first numerical value and the second numerical value to obtain the matching degree; If the first similarity is not greater than the first threshold, setting the matching degree as 0.

4. A body age measuring apparatus characterized by comprising: The body age measuring device comprises: An acquisition unit configured to acquire basic body data and current body composition data of a user to be detected, wherein the basic body data comprises actual age, gender, height and weight, and the current body composition data comprises body fat rate, muscle mass, water content, total fat amount, bone content, visceral fat content and basal metabolic rate; A first calculation unit configured to calculate a matching degree of the user to be detected in a matching library according to the basic body data and the current body composition data; A second calculation unit configured to, when the matching degree is greater than a second threshold, acquire an initial body age corresponding to a maximum matching degree and a plurality of reference body composition data; subtract reference body composition data corresponding to the same body composition data from current body composition data to obtain a data difference value; if the data difference value is greater than a third threshold, take the reference body composition data corresponding to the data difference value as adjusted reference body composition data; acquire a body age influence factor corresponding to the adjusted reference body composition data, wherein the body age influence factor represents the influence degree of the body composition on the body age; multiply the adjusted reference body composition data by the body age influence factor to obtain a first age influence value; multiply the current body composition data by the body age influence factor to obtain a second age influence value; subtract the first age influence value from the second age influence value to obtain an age adjustment value; subtract the age adjustment value from the initial body age to obtain a reference body age; take the reference body age as the body age of the user to be detected; and input the body fat rate, the muscle mass, the water content, the total fat amount, the bone content, the visceral fat content and the basal metabolic rate into a preset model to obtain a body age output by the preset model; The preset model is: ； ； wherein, denotes the body age, denotes the actual age, denotes the body fat rate, denotes the muscle mass, denotes the basal metabolic rate, denotes the water content, denotes the total fat mass, denotes the bone content, denotes the visceral fat content, , , , , , and denote the tuning factors for each body composition data item, denote the tuning factors for the interaction terms, denote the non-linear function of the i-th current body composition data denote the i-th current body composition data, denote the standard value corresponding to the current body composition data, and denote the weight factors.

5. A terminal device, characterized by comprising: The terminal device comprises a memory, a processor and a body age measuring program stored on the memory and executable on the processor, and the body age measuring program is configured to implement the steps in the body age measuring method according to any one of claims 1 to 3.

6. A computer-readable storage medium storing a computer program, the computer program comprising instructions that, when executed by a computer, cause the computer to perform the method of any one of claims 1 to 5. The computer program is executed by the processor to implement the steps in the body age measuring method according to any one of claims 1 to 3.

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